Load raw data, annotate probes using biomaRt and load SFARI genes

Filtering genes that are not expressed in any of the samples

# Load csvs
datExpr = read.csv('./../raw_data/RNAseq_ASD_datExpr.csv', row.names=1)
datMeta = read.csv('./../raw_data/RNAseq_ASD_datMeta.csv')
SFARI_genes = read_csv('./../working_data/SFARI_genes_01-15-2019.csv')

# Make sure datExpr and datMeta columns/rows match
rownames(datMeta) = paste0('X', datMeta$Dissected_Sample_ID)
if(!all(colnames(datExpr) == rownames(datMeta))){
  print('Columns in datExpr don\'t match the rowd in datMeta!')
}

# Make data transformation in datMeta
rownames(datMeta) = datMeta$Dissected_Sample_ID
datMeta$Dx = factor(datMeta$Diagnosis_, levels=c('CTL', 'ASD'))
datMeta$Sex = as.factor(datMeta$Sex)
datMeta$Brain_Bank = as.factor(datMeta$Brain_Bank)
datMeta$Brain_Region = as.factor(datMeta$Region)
datMeta$Brain_lobe = 'Occipital'
datMeta$Brain_lobe[datMeta$Brain_Region %in% c('BA4_6', 'BA9', 'BA24', 'BA44_45')] = 'Frontal'
datMeta$Brain_lobe[datMeta$Brain_Region %in% c('BA3_1_2_5', 'BA7')] = 'Parietal'
datMeta$Brain_lobe[datMeta$Brain_Region %in% c('BA38', 'BA39_40', 'BA20_37', 'BA41_42_22')] = 'Temporal'
datMeta$Brain_lobe=factor(datMeta$Brain_lobe, levels=c('Frontal', 'Temporal', 'Parietal', 'Occipital'))
datMeta$RIN[is.na(datMeta$RIN)] = mean(datMeta$RIN, na.rm=T)

# Annotate probes
getinfo = c('ensembl_gene_id','external_gene_id','chromosome_name','start_position',
            'end_position','strand','band','gene_biotype','percentage_gc_content')
mart = useMart(biomart='ENSEMBL_MART_ENSEMBL',
               dataset='hsapiens_gene_ensembl',
               host='feb2014.archive.ensembl.org') ## Gencode v19
datProbes = getBM(attributes=getinfo, filters=c('ensembl_gene_id'), values=rownames(datExpr), mart=mart)
datProbes = datProbes[match(rownames(datExpr), datProbes$ensembl_gene_id),]
datProbes$length = datProbes$end_position-datProbes$start_position

#################################################################################
# FILTERS:

# 1 Filter probes with start or end position missing (filter 5)
# These can be filtered without probe info, they have weird IDS that don't start with ENS
to_keep = !is.na(datProbes$length)
datProbes = datProbes[to_keep,]
datExpr = datExpr[to_keep,]
rownames(datProbes) = datProbes$ensembl_gene_id

# 2. Filter samples from ID AN03345 (filter 2)
to_keep = (datMeta$Subject_ID != 'AN03345')
datMeta = datMeta[to_keep,]
datExpr = datExpr[,to_keep]

# 3. Filter genes with zeros in all their entries (filter 13795)
to_keep = rowSums(datExpr)>0
datProbes = datProbes[to_keep,]
datExpr = datExpr[to_keep,]

#################################################################################
# Annotate SFARI genes

# Get ensemble IDS for SFARI genes
mart = useMart(biomart='ENSEMBL_MART_ENSEMBL',
               dataset='hsapiens_gene_ensembl',
               host='feb2014.archive.ensembl.org') ## Gencode v19

gene_names = getBM(attributes=c('ensembl_gene_id', 'hgnc_symbol'), filters=c('hgnc_symbol'), 
                   values=SFARI_genes$`gene-symbol`, mart=mart) %>% 
                   mutate('gene-symbol'=hgnc_symbol, 'ID'=as.character(ensembl_gene_id)) %>% 
                   dplyr::select('ID', 'gene-symbol')

SFARI_genes = left_join(SFARI_genes, gene_names, by='gene-symbol')

#################################################################################
# Apply log2 transformation to the data
datExpr = log2(datExpr+1)


datExpr_backup = datExpr

print(paste0('Number of genes: ', nrow(datExpr)))
## [1] "Number of genes: 49882"
print('SFARI genes count by score')
## [1] "SFARI genes count by score"
table(SFARI_genes$`gene-score`)
## 
##   1   2   3   4   5   6 
##  29  82 209 538 191  25
print('Samples count by lobe')
## [1] "Samples count by lobe"
table(datMeta$Brain_lobe)
## 
##   Frontal  Temporal  Parietal Occipital 
##        21        20        22        23
rm(getinfo, to_keep, gene_names, mart)

Calculate/Load Differential Expression metrics for all genes, load SFARI dataset

if(!file.exists('./../working_data/genes_ASD_DE_info_raw_log2.csv')) {
  
  # Calculate differential expression for ASD
  mod = model.matrix(~ Diagnosis_, data=datMeta)
  corfit = duplicateCorrelation(datExpr, mod, block=datMeta$Subject_ID)
  lmfit = lmFit(datExpr, design=mod, block=datMeta$Subject_ID, correlation=corfit$consensus)
  
  fit = eBayes(lmfit, trend=T, robust=T)
  top_genes = topTable(fit, coef=2, number=nrow(datExpr))
  genes_DE_info = top_genes[match(rownames(datExpr), rownames(top_genes)),] %>%
                  mutate('ID'=rownames(datExpr)) %>% left_join(SFARI_genes, by='ID')
  
  write_csv(genes_DE_info, path='./../working_data/genes_ASD_DE_info_raw_log2.csv')
  
  rm(mod, corfit, lmfit, fit, top_genes)
} else {
genes_DE_info = read_csv('./../working_data/genes_ASD_DE_info_raw_log2.csv')
}

genes_DE_info = genes_DE_info %>% dplyr::select(ID, logFC, AveExpr, t, P.Value, adj.P.Val, 
                                                B, status, `gene-score`, syndromic)

rm(datSeq, datProbes)

Changes in PCA plots for different filtering thresholds

lfc=-1 means no filtering at all, the rest of the filterings include on top of the defined lfc, an adjusted p-value lower than 0.05

lfc_list = c(seq(0, 2, 0.05))

n_genes = nrow(datExpr)

# Calculate PCAs
datExpr_pca_samps = datExpr %>% data.frame %>% t %>% prcomp(scale.=TRUE)
datExpr_pca_genes = datExpr %>% data.frame %>% prcomp(scale.=TRUE)

# Initialice DF to save PCA outputs
pcas_samps = datExpr_pca_samps$x %>% data.frame %>% dplyr::select(PC1:PC2) %>% 
             mutate('ID'=colnames(datExpr), 'lfc'=-1, PC1=scale(PC1), PC2=scale(PC2))
pcas_genes = datExpr_pca_genes$x %>% data.frame %>% dplyr::select(PC1:PC2) %>% 
             mutate('ID'=rownames(datExpr), 'lfc'=-1, PC1=scale(PC1), PC2=scale(PC2))

pca_samps_old = pcas_samps
pca_genes_old = pcas_genes

for(lfc in lfc_list){
  
  # Filter DE genes with iteration's criteria
  DE_genes = genes_DE_info %>% filter(adj.P.Val<0.05 & abs(logFC)>lfc)
  datExpr_DE = datExpr %>% data.frame %>% filter(rownames(.) %in% DE_genes$ID)
  n_genes = c(n_genes, nrow(DE_genes))
  
  # Calculate PCAs
  datExpr_pca_samps = datExpr_DE %>% t %>% prcomp(scale.=TRUE)
  datExpr_pca_genes = datExpr_DE %>% prcomp(scale.=TRUE)

  # Create new DF entries
  pca_samps_new = datExpr_pca_samps$x %>% data.frame %>% dplyr::select(PC1:PC2) %>% 
                  mutate('ID'=colnames(datExpr), 'lfc'=lfc, PC1=scale(PC1), PC2=scale(PC2))
  pca_genes_new = datExpr_pca_genes$x %>% data.frame %>% dplyr::select(PC1:PC2) %>% 
                  mutate('ID'=DE_genes$ID, 'lfc'=lfc, PC1=scale(PC1), PC2=scale(PC2))  
  
  # Change PC sign if necessary
  if(cor(pca_samps_new$PC1, pca_samps_old$PC1)<0) pca_samps_new$PC1 = -pca_samps_new$PC1
  if(cor(pca_samps_new$PC2, pca_samps_old$PC2)<0) pca_samps_new$PC2 = -pca_samps_new$PC2
  if(cor(pca_genes_new$PC1, pca_genes_old[pca_genes_old$ID %in% pca_genes_new$ID,]$PC1 )<0){
    pca_genes_new$PC1 = -pca_genes_new$PC1
  }
  if(cor(pca_genes_new$PC2, pca_genes_old[pca_genes_old$ID %in% pca_genes_new$ID,]$PC2 )<0){
    pca_genes_new$PC2 = -pca_genes_new$PC2
  }
  
  pca_samps_old = pca_samps_new
  pca_genes_old = pca_genes_new
  
  # Update DFs
  pcas_samps = rbind(pcas_samps, pca_samps_new)
  pcas_genes = rbind(pcas_genes, pca_genes_new)
  
}

# Add Diagnosis/SFARI score information
pcas_samps = pcas_samps %>% mutate('ID'=substring(ID,2)) %>% 
             left_join(datMeta, by=c('ID'='Dissected_Sample_ID')) %>%
             dplyr::select(ID, PC1, PC2, lfc, Diagnosis_, Brain_lobe)
## Warning: Column `ID`/`Dissected_Sample_ID` joining character vector and
## factor, coercing into character vector
pcas_genes = pcas_genes %>% left_join(SFARI_genes, by='ID') %>% 
             mutate('score'=as.factor(`gene-score`)) %>%
             dplyr::select(ID, PC1, PC2, lfc, score)

# Plot change of number of genes
ggplotly(data.frame('lfc'=lfc_list, 'n_genes'=n_genes[-1]) %>% ggplot(aes(x=lfc, y=n_genes)) + 
         geom_point() + geom_line() + theme_minimal() + 
         ggtitle('Number of remaining genes when modifying filtering threshold'))
rm(datExpr_pca_genes, datExpr_pca_samps, DE_genes, datExpr_DE, pca_genes_new, pca_samps_new, 
   pca_genes_old, pca_samps_old, lfc_list, lfc)

Samples

Note: PC values get smaller as Log2 fold change increases, so on each iteration the values were scaled so it would be easier to compare between frames

Coloured by Diagnosis:

ggplotly(pcas_samps %>% ggplot(aes(PC1, PC2, color=Diagnosis_)) + geom_point(aes(frame=lfc, ids=ID)) + 
         theme_minimal() + ggtitle('Samples PCA plot modifying filtering threshold'))

Coloured by brain region:

No recognisable pattern

ggplotly(pcas_samps %>% ggplot(aes(PC1, PC2, color=Brain_lobe)) + geom_point(aes(frame=lfc, ids=ID)) + 
         theme_minimal() + ggtitle('Samples PCA plot modifying filtering threshold'))

Genes

SFARI genes coloured by score

pcas_sfari_genes = pcas_genes %>% filter(!is.na(score)) %>% dplyr::select(-'score')

complete_sfari_df = expand.grid(unique(pcas_sfari_genes$ID), unique(pcas_sfari_genes$lfc))
colnames(complete_sfari_df) = c('ID', 'lfc')

pcas_sfari_genes = pcas_sfari_genes %>% right_join(complete_sfari_df, by=c('ID','lfc')) %>% 
                   left_join(SFARI_genes, by='ID') %>% 
                   mutate('score'=as.factor(`gene-score`), 'syndromic'=as.factor(syndromic))
pcas_sfari_genes[is.na(pcas_sfari_genes)] = 0 # Fix for ghost points
  
ggplotly(pcas_sfari_genes %>% ggplot(aes(PC1, PC2, color=score)) + 
         geom_point(aes(frame=lfc, ids=ID), alpha=0.6) + theme_minimal() + 
         ggtitle('Genes PCA plot modifying filtering threshold'))

Most of the genes get filtered out by the first adjusted p-value<0.05 filter (including all the genes with score=1), but the proportion of genes left after the first cut is higher for all scores (except 1) and they seem to be generally filtered out after as well

table(SFARI_genes$`gene-score`[SFARI_genes$ID %in% genes_DE_info$ID[genes_DE_info$adj.P.Val<0.05]])
## 
##  2  3  4  5  6 
##  3 18 51 28  2
# Calculate percentage of genes remaining on each lfc by each score
score_count_by_lfc = pcas_genes %>% filter(!is.na(score)) %>% group_by(lfc, score) %>% tally %>% ungroup
score_count_pcnt = score_count_by_lfc %>% filter(lfc==-1) %>% mutate('n_init'=n) %>%
                   dplyr::select(score, n_init) %>% right_join(score_count_by_lfc, by='score') %>%
                   mutate('pcnt'=round(n/n_init*100, 2)) %>% filter(lfc!=-1)

# Complete missing entries with zeros
complete_score_count_pcnt = expand.grid(unique(score_count_pcnt$lfc), unique(score_count_pcnt$score))
colnames(complete_score_count_pcnt) = c('lfc', 'score')
score_count_pcnt = full_join(score_count_pcnt, complete_score_count_pcnt, by=c('lfc','score')) %>%
                   dplyr::select(score, lfc, n, pcnt)
score_count_pcnt[is.na(score_count_pcnt)] = 0

# Join counts by score and all genes
all_count_pcnt = pcas_genes %>% group_by(lfc) %>% tally  %>% filter(lfc!=-1) %>% 
                 mutate('pcnt'=round(n/nrow(datExpr)*100, 2), 'score'='All')
score_count_pcnt = rbind(score_count_pcnt, all_count_pcnt)

ggplotly(score_count_pcnt %>% ggplot(aes(lfc, pcnt, color=score)) + geom_point() + geom_line() + 
         scale_colour_manual(palette=gg_colour_hue) + theme_minimal() + 
         ggtitle('% of points left after each increase in log2 fold change'))
rm(score_count_by_lfc, complete_score_count_pcnt)

SFARI genes coloured by syndromic tag

Most syndromic genes get filtered out with the p-value threshold, the remaining ones don’t seem to have a very different behaviour to the rest of the data, perhaps they survive a bit longer.

ggplotly(pcas_sfari_genes %>% ggplot(aes(PC1, PC2, color=ordered(syndromic, levels=c(1,0)))) + 
         geom_point(aes(frame=lfc, ids=ID), alpha=0.6) + theme_minimal() + 
         scale_colour_manual(palette=gg_colour_hue) +
         ggtitle('Genes PCA plot modifying filtering threshold'))
# Calculate percentage of syndromic genes remaining on each lfc
syndromic_count_by_lfc = pcas_sfari_genes %>% filter(syndromic==1 & PC1!=0) %>% group_by(lfc) %>% tally %>% 
                         ungroup %>% filter(lfc!=-1) %>% 
                         mutate('pcnt' = round(n/nrow(SFARI_genes[SFARI_genes$syndromic==1,])*100,2), 'score'='syndromic')

# Complete missing entires with zeros and add stats for all genes for comparison
syndromic_count_by_lfc = data.frame('lfc' = unique(pcas_genes$lfc), 'score'='syndromic') %>% filter(lfc!=-1) %>% 
                         full_join(syndromic_count_by_lfc, by=c('lfc','score')) %>% replace(.,is.na(.),0) %>%
                         rbind(all_count_pcnt) %>% mutate('score'=ordered(score, levels=c('syndromic','All')))


ggplotly(syndromic_count_by_lfc %>% ggplot(aes(lfc, pcnt, color=score)) + geom_point() + geom_line() + 
         scale_colour_manual(palette=gg_colour_hue) + theme_minimal() + 
         ggtitle('% of points left after each increase in log2 fold change'))

All genes together

ggplotly(pcas_genes %>% ggplot(aes(PC1, PC2)) + geom_point(aes(frame=lfc, ids=ID, alpha=0.3)) + 
         theme_minimal() + ggtitle('Genes PCA plot modifying filtering threshold'))